000			02862naaaa2200313uu 4500
001			https://directory.doabooks.org/handle/20.500.12854/77893
005			20220220075559.0
020			_a9780262301183
020			_a9780262017183
041	0		_aEnglish
042			_adc
072		7	_aUMB _2bicssc
072		7	_aUYQM _2bicssc
100	1		_aSchapire, Robert E. _4auth
700	1		_aFreund, Yoav _4auth
245	1	0	_aBoosting : Foundations and Algorithms
260			_aCambridge _bThe MIT Press _c2012
300			_a1 electronic resource (544 p.)
506	0		_aOpen Access _2star _fUnrestricted online access
520			_aAn accessible introduction and essential reference for an approach to machine learning that creates highly accurate prediction rules by combining many weak and inaccurate ones. Boosting is an approach to machine learning based on the idea of creating a highly accurate predictor by combining many weak and inaccurate “rules of thumb.” A remarkably rich theory has evolved around boosting, with connections to a range of topics, including statistics, game theory, convex optimization, and information geometry. Boosting algorithms have also enjoyed practical success in such fields as biology, vision, and speech processing. At various times in its history, boosting has been perceived as mysterious, controversial, even paradoxical. This book, written by the inventors of the method, brings together, organizes, simplifies, and substantially extends two decades of research on boosting, presenting both theory and applications in a way that is accessible to readers from diverse backgrounds while also providing an authoritative reference for advanced researchers. With its introductory treatment of all material and its inclusion of exercises in every chapter, the book is appropriate for course use as well. The book begins with a general introduction to machine learning algorithms and their analysis; then explores the core theory of boosting, especially its ability to generalize; examines some of the myriad other theoretical viewpoints that help to explain and understand boosting; provides practical extensions of boosting for more complex learning problems; and finally presents a number of advanced theoretical topics. Numerous applications and practical illustrations are offered throughout.
540			_aCreative Commons _fby-nc-nd/4.0 _2cc _4http://creativecommons.org/licenses/by-nc-nd/4.0
546			_aEnglish
650		7	_aAlgorithms & data structures _2bicssc
650		7	_aMachine learning _2bicssc
653			_aArtificial intelligence
653			_aAlgorithms and data structures
856	4	0	_awww.oapen.org _uhttp://mitpress.mit.edu/9780262017183 _70 _zDOAB: download the publication
856	4	0	_awww.oapen.org _uhttps://directory.doabooks.org/handle/20.500.12854/77893 _70 _zDOAB: description of the publication
999			_c74517 _d74517